A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree
Buildings are among the most important elements in the urban structure that can affect urban planning. Therefore, it is important to create the footprint of the buildings, especially in developing cities, which is highly time-consuming and costly. Although LiDAR technology has already been used for...
| Үндсэн зохиолчид: | , , , , , |
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| Формат: | Өгүүллэг |
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Elsevier
2022
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| _version_ | 1825938333925113856 |
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| author | Jifroudi, Hamidreza Maskani Mansor, Shattri Pradhan, Biswajeet Abdul Halin, Alfian Ahmad, Noordin Abdullah, Ahmad Fikri |
| author_facet | Jifroudi, Hamidreza Maskani Mansor, Shattri Pradhan, Biswajeet Abdul Halin, Alfian Ahmad, Noordin Abdullah, Ahmad Fikri |
| author_sort | Jifroudi, Hamidreza Maskani |
| collection | UPM |
| description | Buildings are among the most important elements in the urban structure that can affect urban planning. Therefore, it is important to create the footprint of the buildings, especially in developing cities, which is highly time-consuming and costly. Although LiDAR technology has already been used for this purpose, the need to process voluminous amounts of noisy data and make building footprint extraction in accurate. In this study, we propose a step-by-step analysis of LiDAR data using a rule-based algorithm called DB-creator in order to automatically create building footprints. DB-creator was specifically chose as it does not require external data or region information to construct the footprints. The constructed footprints based from the algorithm was compared with manually created ground truth building footprints to assess accuracy. From experimental results, RMSE for urban and rural areas were ± 0.62 m and ± 0.28 m, respectively, which is highly accurate considering LiDAR’s a 0.5 m surveying distance between two points and 0.6 m distance between rows. Moreover, the kappa coefficient were 0.948 and 0.958 for the urban and rural areas, respectively (which are confirmed by T values of 150.204 and 255.553 at p ≤ 0.01 for the urban and rural areas, respectively). The Standard Errors respectively obtained for urban and rural areas were 0.001 and 0.002, reflecting slight internal variations between the built footprint maps. This also highlights the certainty of the kappa coefficient, indicating that extraction of building footprints is highly accurate. |
| first_indexed | 2024-04-09T03:50:00Z |
| format | Article |
| id | upm.eprints-100240 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-04-09T03:50:00Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-1002402024-03-18T04:59:52Z http://psasir.upm.edu.my/id/eprint/100240/ A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree Jifroudi, Hamidreza Maskani Mansor, Shattri Pradhan, Biswajeet Abdul Halin, Alfian Ahmad, Noordin Abdullah, Ahmad Fikri Buildings are among the most important elements in the urban structure that can affect urban planning. Therefore, it is important to create the footprint of the buildings, especially in developing cities, which is highly time-consuming and costly. Although LiDAR technology has already been used for this purpose, the need to process voluminous amounts of noisy data and make building footprint extraction in accurate. In this study, we propose a step-by-step analysis of LiDAR data using a rule-based algorithm called DB-creator in order to automatically create building footprints. DB-creator was specifically chose as it does not require external data or region information to construct the footprints. The constructed footprints based from the algorithm was compared with manually created ground truth building footprints to assess accuracy. From experimental results, RMSE for urban and rural areas were ± 0.62 m and ± 0.28 m, respectively, which is highly accurate considering LiDAR’s a 0.5 m surveying distance between two points and 0.6 m distance between rows. Moreover, the kappa coefficient were 0.948 and 0.958 for the urban and rural areas, respectively (which are confirmed by T values of 150.204 and 255.553 at p ≤ 0.01 for the urban and rural areas, respectively). The Standard Errors respectively obtained for urban and rural areas were 0.001 and 0.002, reflecting slight internal variations between the built footprint maps. This also highlights the certainty of the kappa coefficient, indicating that extraction of building footprints is highly accurate. Elsevier 2022-03-31 Article PeerReviewed Jifroudi, Hamidreza Maskani and Mansor, Shattri and Pradhan, Biswajeet and Abdul Halin, Alfian and Ahmad, Noordin and Abdullah, Ahmad Fikri (2022) A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree. Measurement, 192. art. no. 110781. pp. 1-13. ISSN 0263-2241 https://www.sciencedirect.com/science/article/pii/S0263224122000811 10.1016/j.measurement.2022.110781 |
| spellingShingle | Jifroudi, Hamidreza Maskani Mansor, Shattri Pradhan, Biswajeet Abdul Halin, Alfian Ahmad, Noordin Abdullah, Ahmad Fikri A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree |
| title | A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree |
| title_full | A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree |
| title_fullStr | A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree |
| title_full_unstemmed | A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree |
| title_short | A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree |
| title_sort | new approach to derive buildings footprint from light detection and ranging data using rule based learning techniques and decision tree |
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